Organizations who market products and services indirectly (“indirect businesses”) frequently struggle to collect complete usage data at the consumer level. That is, while the indirect business may know in the aggregate that they have a particular number of consumers and the total consumption of their products or services, they are unable to determine what amount of each product or service is consumed by a particular consumer. Even as organizations possess ever increasing volumes of information about their consumers, indirect businesses like film production companies, sports leagues, and similar organizations do not have complete information about how each consumer is using their products and services. This is, in part, because there are numerous intermediating companies between indirect businesses and their ultimate consumer, such as TV networks, internet streaming services, merchandise shops, and others. Frequently, these intermediating companies can and do collect direct usage metrics for individual users, but they are either unable or unwilling to share that data. While the problem is particularly acute for media companies, the problem of indirect sales is present across many other industries, such as movie studios who sell through movie theaters, etc. Often the only reliable source of usage metrics comes from survey companies that collect data from a reasonably sized and/or representative sample of consumers that agree to have certain behaviors monitored or recorded. Even if these survey companies are willing to share aggregate statistics, and extrapolated estimates of total behavior, many such companies are still unwilling or unable to share per-consumer survey data, even for their panel members.
Even without per-consumer consumption data, many indirect businesses nevertheless have considerable databases with other consumer information. This information can come from manually submitted preference or profile data, such as a consumer's name, address, birthdate, favorite team or show, etc. In addition, indirect businesses often log information regarding interactions consumers have with the organization's products and services that are under the organization's control, such as webpage views, merchandise purchased, digital subscriptions, etc. These organizations may also be able to obtain consumer data from affiliates and business partners whose incentives are aligned with the indirect business's or who have contractual arrangements that permit sharing of certain consumer data.
Nevertheless, extremely valuable information—per-user consumption of their primary products or services—often remains out of reach. Because these indirect businesses cannot identify each individual consumer's consumption levels, they are often unable to identify consumers that are high-volume purchasers and users, understand how each consumer's interests affects his or her consumption, and are missing important information to shape future decisions about marketing and new products and services for its consumers. This long felt need to estimate per-product or per-service consumption, while preserving consumer privacy, is solved by the present disclosed technology, among other benefits.
The technology disclosed herein meets this need by using data within an indirect business's possession, combined with per-consumer data in the possession of a survey company to train a machine learning model to estimate, based on information in the indirect business's possession, per-consumer consumption values. The present disclosed technology further provides for combining the indirect business's data and the survey company's data on a system isolated from the indirect business. The indirect business can define machine learning or statistical models to be trained on the combined data, validate the trained model, and use it to estimate consumption by consumers in its own records without accessing the combined information. In this way, the per-consumer consumption information in possession of a survey company is not exposed to the direct business, the survey panel member's privacy is preserved, and the indirect business is able to obtain accurate estimates of each consumer's consumption of their products and services.
The present disclosed technology further includes a system and method for using consumer data to produce actionable consumer segmentation, including in conjunction with the modeling process described above. The present disclosed technology further provides methods to attribute revenue and/or profit derived from numerous sources to each consumer on a per-consumer level.
While certain embodiments of the present disclosure are described with respect to indirect businesses determining media consumption, and the use of survey panel data, it is understood that the full scope of the disclosed technology encompasses the use of similar techniques for indirect businesses determining consumption of other products or services. Indeed, embodiments of the present disclosure are suitable for producing models to estimate product and/or service consumption, or any other consumer behavior, based on combining internal available data with actual measured values of that consumer behavior in the possession of another organization.